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Chemical reaction optimization for solving shortest common supersequence problem.

C M Khaled Saifullah1, Md Rafiqul Islam1

  • 1Computer Science and Engineering Discipline, Khulna University, Khulna 9208, Bangladesh.

Computational Biology and Chemistry
|June 15, 2016
PubMed
Summary
This summary is machine-generated.

A new Chemical Reaction Optimization (CRO) algorithm, CRO_SCS, effectively solves the NP-hard Shortest Common Supersequence (SCS) problem. This approach outperforms existing methods in finding shorter supersequences and reducing computation time.

Keywords:
AlgorithmChemical reaction optimizationMeta-heuristicsNP-hard problemShortest common supersequence

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Area of Science:

  • Computational biology
  • Bioinformatics algorithms
  • Optimization techniques

Background:

  • The Shortest Common Supersequence (SCS) problem is a computationally challenging NP-hard problem with significant applications in data compression, database query optimization, and bioinformatics.
  • Exact algorithms struggle with larger SCS instances due to NP-hardness, necessitating heuristic and meta-heuristic solutions.

Purpose of the Study:

  • To introduce a novel meta-heuristic algorithm, CRO_SCS, based on Chemical Reaction Optimization (CRO) for solving the SCS problem.
  • To adapt and redesign CRO's reaction operators and introduce a new reform function specifically for SCS problem-solving.

Main Methods:

  • The proposed CRO_SCS algorithm is inspired by the principles of chemical reactions.
  • Reaction operators and a reform function were specifically designed and implemented within the CRO framework to address the SCS problem.

Main Results:

  • CRO_SCS demonstrated superior performance compared to Enhanced Beam Search (IBS_SCS), Deposition and Reduction (DR), Ant Colony Optimization (ACO), and Artificial Bee Colony (ABC) algorithms.
  • The proposed algorithm achieved better average results in terms of supersequence length, execution time, and standard deviation.

Conclusions:

  • The CRO_SCS algorithm offers a promising and effective meta-heuristic approach for solving the Shortest Common Supersequence problem.
  • The findings suggest that CRO-based methods can be successfully adapted for complex string-related optimization tasks in bioinformatics and other fields.